Features of TensorFlow:

• TensorFlow provides a flexible architecture that allows users to define and train various types of neural network models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more.
• It offers extensive support for parallel computing and distributed training, enabling efficient utilization of resources and scalability across multiple GPUs and CPUs.
• TensorFlow includes a rich collection of pre-built components and modules, such as layers, optimizers, and metrics, streamlining the development process and reducing the need for manual implementation.
• It features robust tools for model evaluation, debugging, and visualization, empowering users to analyze model performance, identify errors, and gain insights into the underlying data patterns.

      
 source pip-tensorflow/bin/activate
      
 python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__)"
      
python
     
>>>import tensorflow as tf
# Check TensorFlow version
print(f"TensorFlow version: {tf.__version__}")
# Create a simple TensorFlow computation
hello = tf.constant('Hello, TensorFlow!')
print(hello.numpy())
# Test basic tensor operations
a = tf.constant(2)
b = tf.constant(3)
c = a + b
print(f"The result of a + b is: {c.numpy()}")